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Automatic spine recognition which supports quantitative measurement is essential in numerous spine related applications in orthopedics, neurology, and oncology. The task of automatic spine recognition is to extract the set of numerical parameters that can uniquely determine the global structure of the spine and certain local structures of the vertebrae. Currently, spine recognition is often simplified as vertebra detection, which extracts the locations and labels of the vertebrae in input images.
Spine recognition is a challenging problem in spine image analysis. In the present application spine recognition is accomplished, in part, by using image appearances. Image appearance is defined as a set of geometric parameters that determine the local and global spine structures. The parameters obtained from spine recognition provides a unified geometry model that can be shared among spine structures from different modalities, different image views, and different formats. The appearance parameters for a spine can be used in quantitative measurement systems for diagnostic purposes and can be stored/retrieved by medical PACS systems. The main difficulties of spine recognition arise from the high variability of image appearance due to modality differences or shape deformations: 1) Multiple modalities. The image resolution, contrast, and appearance for the same spine structure are very different when it is exposed to CT, or T1/T2 weighted MR images. 2) High repetition. The appearances of vertebrae and intervertebral discs are highly repetitive which often leads to mismatching/misrecognition. 3) Various poses. The vertebrae sizes and orientations are highly diverse in pathological data therefore regular detectors such as appearance detectors are insufficient to match all vertebra. 4) Complex shape composition. The spine is composed of local vertebrae and the compositional shape can be highly twisted and cannot be described by simple geometric models like curves and surfaces. Therefore, current methods of recognizing local vertebral structures and global spine shapes are often separately done by spine detection and spine shape matching techniques.